Crop Planning using Stochastic Visual Optimization

As the world population increases and arable land decreases, it becomes vital to improve the productivity of the agricultural land available. Given the weather and soil properties, farmers need to take critical decisions such as which seed variety to plant and in what proportion, in order to maximize productivity. These decisions are irreversible and any unusual behavior of external factors, such as weather, can have catastrophic impact on the productivity of crop. A variety which is highly desirable to a farmer might be unavailable or in short supply, therefore, it is very critical to evaluate which variety or varieties are more likely to be chosen by farmers from a growing region in order to meet demand. In this paper, we present our visual analytics tool, ViSeed, showcased on the data given in Syngenta 2016 crop data challenge 1 . This tool helps to predict optimal soybean seed variety or mix of varieties in appropriate proportions which is more likely to be chosen by farmers from a growing region. It also allows to analyse solutions generated from our approach and helps in the decision making process by providing insightful visualizationsComment: 5 page

Crop Yield Prediction Using Deep Neural Networks

Crop yield is a highly complex trait determined by multiple factors such as genotype, environment, and their interactions. Accurate yield prediction requires fundamental understanding of the functional relationship between yield and these interactive factors, and to reveal such relationship requires both comprehensive datasets and powerful algorithms. In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017. As one of the winning teams, we designed a deep neural network (DNN) approach that took advantage of state-of-the-art modeling and solution techniques. Our model was found to have a superior prediction accuracy, with a root-mean-square-error (RMSE) being 12% of the average yield and 50% of the standard deviation for the validation dataset using predicted weather data. With perfect weather data, the RMSE would be reduced to 11% of the average yield and 46% of the standard deviation. We also performed feature selection based on the trained DNN model, which successfully decreased the dimension of the input space without significant drop in the prediction accuracy. Our computational results suggested that this model significantly outperformed other popular methods such as Lasso, shallow neural networks (SNN), and regression tree (RT). The results also revealed that environmental factors had a greater effect on the crop yield than genotype.Comment: 9 pages, Presented at 2018 INFORMS Conference on Business Analytics and Operations Research (Baltimore, MD, USA). One of the winning solutions to the 2018 Syngenta Crop Challeng

Opportunities for Precision Agriculture in Serbia

The aim of this paper is to analyze the factors leading to low adoption rate of precision farming in Serbia and to describe steps being taken by BioSense institute to increase it. The majority of the arable land in Serbia is grown by small family owned and operated farms most of which are in the range of 2 to 5 ha making them highly unsustainable. Only 16% of the arable land is managed by agricultural companies and cooperatives. We believe that the adoption of advanced technologies with the currently available precision farming solutions is low among the small farmers due to the small size of the agricultural fields and their inability to invest in technologies. Therefore BioSense institute aims to develop low cost, easy to use precision farming solutions that can be applied anywhere regardless of size, the type and age of agricultural machinery used by the farmers and make IT an important tool to drive small farms towards sustainability. With the new applications developed by BioSense all farmers, even small, can benefit from the diffusion of IT into agriculture making precision farming widely accepted in the years to come. In the framework of the “Digital Agriculture of Serbia” program, several technologies are being developed in the areas of nano and microelectronic in-situ sensors, robotic platforms, genotyping/phenotypic, remote sensing (UAS, satellites), internet of things (IoT), and big data analytics as a means to create new information and extract new knowledge that is not otherwise available. A web-based and android-based digital platform named “AgroSense” was recently released for public use and got widely accepted with a large number of large, medium and small farmers registering to the system. The platform brings the benefits of IT to the end users providing free and paid tools (for advanced users) for better decision making. It is also an excellent tool for big data collection that will create new agronomic knowledge. We foresee a great potential for advancing and modernizing farming in Serbia leading towards a more sustainable and environmentally friendly agriculture

Use of DNA-based genetic markers in plant breeding

Genetic markers have been used since the beginnings of plant breeding, but the concept of linkage and recently the availability of molecular markers have offered new and powerful tools that can help to perform the traditional tasks of selection or that can change the traditional breeding process. Markers can either be used in a descriptive manner to identify varieties, to study the ‘micro-evolution’ of composite crosses or variety mixtures or to analyse the breeding progress retrospectively in order to learn from the past. The operative use of markers in plant breeding is connected to the selection of parental lines and progeny lines. The possible implementation of these processes stretches from the introgression of specific chromosome fragments to ‘marker-based idiotype breeding’

The potential of waste sorghum (sorghum bicolor) leaves for bioethanol process development using Saccharomyces cerevisiae BY4743.

Masters Degree, University of KwaZulu-Natal, Pietermaritzburg.The limitations of first generation biofuels have prompted the quest for alternative energy sources. Approximately 60 million tonnes of sorghum are generated each year, with 90% being lignocellulosic waste, which is an ideal feedstock for biofuel production. The recalcitrance of lignocellulose often demands harsh pre-treatment conditions and results in the generation of fermentation inhibitors, negatively impacting process yields and economics. In this study, an artificially intelligent model to predict the profile of reducing sugars and all major volatile compounds from microwave assisted chemical pre-treatment of waste sorghum leaves (SL) was developed and validated. The pre-treated substrate was assessed for bioethanol production using Saccharomyces cerevisiae. Monod and modified Gompertz models were generated and the kinetic coefficients were compared with previous studies on different substrates. To develop the Artificial Neural Network (ANN) model, a total of 58 pre-treatment process conditions with varying parameters were experimentally assessed for reducing sugar (RS) and volatile compound production. The pre-treatment input variables consisted of acid concentration, alkali concentration, microwave duration, microwave intensity and solid-to-liquid ratio (S:L). Response Surface Methodology (RSM) was used to optimise RS production from microwave assisted acid pre-treatment of sorghum leaves, giving a coefficient of determination (R2 ) of 0.76, resulting in an optimal yield of 2.74 g RS/g SL. A multilayer perceptron ANN model was used, with a topology of 5-13-13-21. The model was trained using the backpropagation algorithm to minimise the net error value on validation. The model was validated on experimental data and R2 values of up to 0.93 were obtained. The developed model was used to predict the profile of inhibitory compounds under various pre-treatment conditions. Some of these inhibitory compounds were: acetic acid (0-186.26 ng/g SL), furfural (0-240.80 ng/g SL), 5-hydroxy methyl furfural (HMF) (0-19.20 ng/g SL) and phenol (0-7.76 ng/g SL). The developed ANN model was further subjected to knowledge extraction. Findings revealed that furfural and phenol generation during substrate pre-treatment exhibited high sensitivity to acid- and alkali concentration and S:L ratio, while phenol production showed high sensitivity to microwave duration and intensity. Furfural generation during pre-treatment of waste SL was majorly dependent on acid concentration and fit a dosage-response relationship model with a 2.5% HCl threshold. VI The pre-treated sorghum leaves were enzymatically hydrolysed and subsequently assessed for yeast growth and bioethanol production using Saccharomyces cerevisiae BY4743. Kinetic modelling was carried out using the Monod and the modified Gompertz models. Fermentations were carried out with varied initial substrate concentrations (12.5-30.0 g/L). The Monod model fitted well to the experimental data, exhibiting an R2 of 0.95. The model coefficients of maximum specific growth rate (μmax) and Monod constant (Ks) were 0.176 h-1 and 10.11 g/L respectively. Bioethanol production data fitted the modified Gompertz model with an R2 of 0.98. A bioethanol production lag time of 6.31 hours, maximum ethanol production rate of 0.52 g/L/h and a maximum potential bioethanol concentration of 17.15 g/L were obtained. These findings demonstrated that waste SL, commonly considered as post-harvest waste, contain sufficient fermentable sugar which can be recovered from appropriate HCl-based pre-treatment, for use as a low cost energy source for biofuel production. The extracted knowledge from the developed ANN model revealed significant non-linearities between the pre-treatment input conditions and generation of volatile compounds from waste SL. This predictive tool reduces analytical costs in bioprocess development through virtual analytical instrumentation. Monod and modified Gompertz coefficients demonstrated the potential of utilising sorghum leaves for bioethanol production, by providing data for early stage knowledge of the production efficiency of bioethanol production from waste SL. The generated kinetic knowledge of S. cerevisiae growth on waste SL and bioethanol formation in this study is of high importance for process optimisation and scale up towards the commercialisation of this fuel.Only available in English

The climatic challenge: which plants will people use in the next century?

More than 31,000 useful plant species have been documented to fulfil needs and services for humans or the animals and environment we depend on. Despite this diversity, humans currently satisfy most requirements with surprisingly few plant species; for example, just three crops – rice, wheat and maize – comprise more than 50% of plant derived calories. Here, we synthesize the projected impact of global climatic change on useful plants across the spectrum of plant domestication. We illustrate the demographic, spatial, ecophysiological, chemical, functional, evolutionary and cultural traits that are likely to characterise useful plants and their resilience in the next century. Using this framework, we consider a range of possible pathways for future human use of plants. These are centred on two trade-offs: i) diversification versus specialization in the range of species we utilize, and ii) substitutionof the species towards those better suited to future climate versus facilitating adaptation in our existing suite of dominant useful plants. In the coming century, major challenges to agriculture and biodiversity will be dominated by increased climatic variation, shifting species ranges, disruption to biotic interactions, nutrient limitation and emerging pests and pathogens. These challenges must be mitigated, whilst enhancing sustainable production to meet the needs of a growing population and a more resource intensive standard of living. With the continued erosion of biodiversity, our future ability to choose among these pathways and trade-offs is likely to be diminished

Proceedings of the COST SUSVAR/ECO-PB Workshop on organic plant breeding strategies and the use of molecular markers

In many countries,national projects are in progress to investigate the sustainable low-input approach.In the present COST network,these projects are coordinated by means of exchange of materials,establishing common methods for assessment and statistical analyses and by combining national experimental results.The common framework is cereal production in low-input sustainable systems with emphasis on crop diversity.The network is organised into six Working Groups,five focusing on specific research areas and one focusing on the practical application of the research results for variety testing:1)plant genetics and plant breeding,2)biostatistics,3)plant nutrition and soil microbiology,4)weed biology and plant competition,5)plant pathology and plant disease resistance biology and 6)variety testing and certification.It is essential that scientists from many disciplines work together to investigate the complex interactions between the crop and its environment,in order to be able to exploit the natural regulatory mechanisms of different agricultural systems for stabilising and increasing yield and quality.The results of this cooperation will contribute to commercial plant breeding as well as official variety testing,when participants from these areas disperse the knowledge achieved through the EU COST Action